Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis

sustainability

Article Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis

Siqin Tong 1,2, Gang Bao 1,3, Ah Rong 1, Xiaojun Huang 1,3, Yongbin Bao 4 and Yuhai Bao 1,3,*

1 College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China; [email protected] (S.T.); [email protected] (G.B.); [email protected] (A.R.); [email protected] (X.H.) 2 Inner Mongolia Key Laboratory of Disaster and Ecological Security on the Mongolian Plateau, Inner Mongolia Normal University, Hohhot 010022, China 3 Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China 4 School of Environment, Northeast Normal University, Changchun 130024, China; [email protected] * Correspondence: [email protected]; Tel.: +86-133-1471-9512

Received: 18 March 2020; Accepted: 29 April 2020; Published: 2 May 2020

Abstract: Land use/cover change (LUCC) is becoming one of the most important and interesting problems in the study of global environmental change. Identifying the spatiotemporal behavior and associated driving forces behind changes in land use is crucial for the regional sustainable utilization of land resources. In this study, we consider the four municipalities of China (Beijing, Tianjin, Shanghai, and Chongqing) and compare their spatiotemporal changes in land use from 1990 to 2015 by employing intensity analysis and barycenter migration models. We then discuss their driving forces. The results show that the largest reduction and increase variations were mainly concentrated in arable and construction land, respectively. The decrement and increment were the largest in Shanghai, followed by Beijing and Tianjin, and the least in Chongqing. Furthermore, the results of the barycenter migration model indicate that in addition to Beijing, the migration distances of construction land were longer than those of arable land in three other cities. Moreover, the application of intensity analysis revealed that the rate of land use change was also the greatest in Shanghai and the slowest in Chongqing during the whole study period, with all of their arable land being mainly transformed into construction land. The driving force analysis results suggest that the spatial and temporal patterns of land use change were the results of the socio-economic development, national policies, and major events. In other words, where there was a high rate of economic and population growth, the intensity of land use change was relatively large.

Keywords: land use/cover change; spatiotemporal changes; intensity analysis; driving forces; municipalities

1. Introduction Land use/cover change (LUCC) has been recognized as an important part of global climate change and global environmental change research [1,2]. As the most direct signal of the impact of human activities on the Earth’s surface and the natural ecosystem, it is the link between human socio-economic activities and ecological processes [3]. This process is closely related to the terrestrial surface material cycle and life process, aﬀecting biosphere–atmosphere interactions, biodiversity, biogeochemical cycles, and the sustainable utilization of resources and the environment [4]. In the combined system of Population–Resources–Environment–Development (PRED), land resources are

Sustainability 2020, 12, 3687; doi:10.3390/su12093687 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 3687 2 of 21 immovable and non-renewable, placing land characteristics in a central position [5]. With the growth of land development and utilization, the pattern, depth, and intensity of land use are constantly changing, which makes the associated ecological and social problems increasingly serious [6–8]. The International Geosphere–Biosphere Program (IGBP) and the International Human Dimensions Program on Global Environmental Change (IHDP) proposed a detailed LUCC research plan in 1995, which focused on land use/land cover dynamics research at regional and global scales [9]. The formulation of this plan established the direction for research into LUCC throughout the world. LUCC often reflect the relationships between humanity and nature [1]. LUCC is increasingly considered as a key and urgent research topic in the study of global environmental change [10]. It plays an important role in the study of global climate change, the carbon cycle, food security, biodiversity, etc. [11–13]. LUCC is affected by both natural and human activities, while displaying certain regularity [14]. The analysis of LUCC dynamics, the exploration of the associated driving factors, and the associated processes are helpful in understanding the ecosystem services and the changes of the earth system functions. It will also help to guide regional sustainable development [15]. At present, the study of LUCC mainly involves three aspects: (i) The study of land use evolution trends, including trends in the extent, magnitude, and spatial evolution of such changes [16,17], (ii) the analysis of the driving mechanisms behind LUCC [18,19], and (iii) the development of relevant models based on the change processes to undertake simulations and to make predictions about LUCC [20–22]. The monitoring of dynamic land use processes is the basis of the analysis of the driving forces and the simulated predictions, which are of great significance for further research into LUCC [23,24]. The common methods of land use dynamics analysis include the transfer matrix method, mathematical statistics, the structural parameter method, and landscape pattern feature analysis [25]. The transfer matrix is a two-dimensional matrix and is obtained from the transformation relationship of land use status for different phases of the same area. It allows transformation information to be obtained about the location and change in areas of different land types at two time phases [26], and has been widely used in LUCC analysis [25–29]. In addition, there are two special methods that have become popular, namely the land use dynamic degree model and intensity analysis [30–36]. The land use dynamic degree model can be divided into single land use dynamic degree and comprehensive land use dynamic degree. These can quantitatively describe the change rate of land use and play an important role in comparing the regional differences in LUCC and in predicting future trends [30–34]. The concept of intensity analysis, first proposed in 2012 [35,36], is from the perspective of system theory, and is based on the transfer matrix of land use area from different periods. It calculates the conversion area and the changing intensity among land types at the interval level, category level, and transition level. Recently, many researchers have used intensity analysis for LUCC studies [35–39], where it has been shown that it is superior to the dynamic degree model, since the dynamic degree model can only calculate the loss intensity, while intensity analysis can calculate the contribution rate of each category to the total change, the loss and gain of different categories, their intensities for each period, and the conversion extent and intensity between each category [40]. The aggravation of human economic activities and the extensive urbanization processes currently underway largely cause the change of urban land use patterns and even led to land use patterns that are contrary to natural trends [41]. These directly or indirectly change the structure and function of the landscape ecosystem, and have a profound impact on regional biodiversity and ecological processes [42,43]. As the most obvious change of the natural landscape, the process of urban land use change has become an important issue for scientists, planners, and decision makers. Land use changes in the four special municipalities of China, namely Beijing, Tianjin, Shanghai, and Chongqing, have received considerable attention. For example, it has been found that between 1999 and 2017, the built-up area increased and arable and forest land decreased in Beijing [44], where more than 10% of the cropland areas were transformed into built-up areas [45]. This is the main characteristic of land use change in Shanghai, where the expansion of the urban construction land occupied vast areas of farmland and forest land [46]. The proportion of unused land and cultivated land decreased Sustainability 2020, 12, 3687 3 of 21 rapidly, and the proportion of forest land and residential areas increased at a high rate in Chongqing between 1997 and 2009 [47]. However, there has not been a systematic comparative analysis for LUCC considering these four areas together. Therefore, the purpose of this study is using the intensity analysis method to study (a) the land use change over time and space in these four municipalities; (b) the comparison of the speed of land use change (fast or slow); (c) the transformation between each land use category and whether they are active or not; and (d) the discussion of the driving forces behind the associated land use changes. This will provide a reference for developing land use plans, land protection policies, optimizing land utilization structures, and coordinating social and economic development and preserving the ecological environment.

2. Materials and Methods

2.1. Study Area Municipalities are the most important provincial administrative regions in many countries. They usually play an important role in the political, economic, and cultural life of the country. Becoming a municipality in China requires certain conditions to be met, such as: (a) It has obvious advantages in location, economy, and politics, and its economic volume is higher than other major cities in China, (b) the population is not less than 2 million, (c) it has a certain economic basis, at least to reach the level of self-sufficiency, which is convenient for future government regulation and control, etc. At present, China has four municipalities (i.e., Beijing, Shanghai, Tianjin, and Chongqing); Chongqing became a municipality in 1949, and the other three cities were already municipalities in 1949. As the capital of China, Beijing is the center of political, cultural, and international exchange in China, along with having substantial international influence. It has a population of about 21.8 million and had a GDP of 3631 billion USD in 2015. It is located from around 39◦28’ to 41◦05’N, and 115◦25’ to 2 117◦30’E, with a total area of 16,800 km , of which 61.4% is mountainous, being generally high in the northwest and low in the southeast (Figure1). Tianjin is adjacent to Beijing, and it is the economic center of the Bohai Rim region, with a total area of 11,917.26 km2, a population of 13 million, and a GDP of 2530 billion USD in 2015. Shanghai is the economic, financial, trade, and shipping center of China. 2 Located around 120◦51’ to 122◦12’E and 30◦40 to 31◦53’N, with a total area of 6341 km , it is situated in the middle of the coastline of China’s Yangtze River Estuary, and is China’s largest foreign trade port and largest industrial base. Its GDP reached 3986 billion USD in 2015. Chongqing is located at the intersection of the Yangtze River and Jialing River, and is an important industrial and commercial city in southwest China, being located at the junction of economic development in central and western China, between 105◦11’ and 110◦11’E and 28◦10’ to 32◦13’N. Its terrain is dominated by low mountains and hills, with a total area of 82,400 km2. Among the four municipalities, it has the largest population and the lowest GDP, with a population of about 30.5 million and a GDP of 2500 billion USD in 2015. Sustainability 2020, 12, 3687 4 of 21 Sustainability 2019, 11, x FOR PEER REVIEW 4 of 21

Figure 1. Figure 1. TheThe locations locations and and topographies topographies of the of study the areas—Beijing,study areas—Beijing, Tianjin, Shanghai,Tianjin, andShanghai, Chongqing. and 2.2. DataChongqing. Sources and Methodology

2.2.2.2.1. Data Data Sources Source and Methodology

2.2.1. TheData LUCC Source data for 1990, 1995, 2000, 2005, 2010, and 2015 were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn), withThe a 1 LUCC km spatial data for resolution. 1990, 1995, The 2000, primary 2005, 2010, sources and 2015 of the were LUCC provided dataset by in the the Data RESDC Center were for ResourcesLandsat Thematic and MapperEnvironmental/ Enhanced Sciences, Thematic MapperChinese (TM /AcademyETM) images. of TheSciences land use data(RESDC) were (http://www.resdc.cn),obtained by image processing, with a 1 human–machinekm spatial resolution. interaction The primary interpretation, sources dynamicof the LUCC extraction, dataset and in thefield RESDC investigation were Landsat verification. Thematic Mapper/ Enhanced Thematic Mapper (TM/ETM) images. The land Theuse datasetdata were starts obtained with the by national image landproces usesing, database human–machine with a scale ofinteraction 1:100,000, interpretation, using the fast dynamicextraction extraction, method of and human–computer field investigation interaction verification. based on Landsat images with land interpretation symbol,The anddataset then starts updates with to thethe databasenational everyland use five database years. (a) Forwith the a scale spatial of resolution, 1:100,000, takingusing thethe 1fast km extractionraster background method of data human–computer and interpreted interaction land use vector based data on asLandsat the basis images of land with use land dynamic interpretation division, symbol,the spatial and accuracy then updates can be to guaranteed the database on theevery basis five of years. eliminating (a) For the the scale spatial effect resolution, of spatial taking data. By the the 1 kmvector raster and background raster overlaying, data and cutting interpreted can be land used use to obtainvector thedata land as the use basis type distributionof land use dynamic and area division,in each raster the spatial grid [48accuracy]. (b) In can the be specific guaranteed operation on the for basis the updateof eliminating of the database, the scale foreffect example, of spatial by data.comparing By the thevector images and ofraster 2005 overlaying, and 2010, based cutting on can the be land used use to classification obtain the land data use of 2005,type distribution the dynamic andinformation area in each coding raster of land grid use [48]. change (b) In is determinedthe specific andoperation plotted, for which the update reflects of the the land database, use types for of example,the changed by comparing land in 2005 the and images 2010 of at 2005 the same and 2010, time. based (c) In on order the toland ensure use classification the interpretation data of quality 2005, theand dynamic consistency information of the acquired coding data, of land a unified use change quality is check determined and data and integration plotted, which are carried reflects out the for landeach use dataset. types A of large the numberchanged of land field in survey 2005 recordsand 201 and0 at photographsthe same time. are (c) obtained In order in theto ensure early stage the interpretationof research and quality development and consistency of each dataset, of the mostly acquired autumn data, in the a northunified area quality and spring check in and the southdata integrationarea. Then, are the carried accuracy out of for field each survey dataset. and A field larg recordse number is verified of field bysurvey random records sampling and photographs according to arethe obtained proportion in ofthe 10% early of stage the counties. of research The and comprehensive development evaluation of each dataset, accuracy mostly of the autumn major class in the of northland usearea type and isspring 94.3%, in andthe south more area. than Then, 91.2% the for accu theracy secondary of field subclasses survey and of field land records use types is verified [48,49]. byThe random flowchart sampling of the data according acquisition to the process proportion is shown of in 10% Figure of2 .the counties. The comprehensive evaluation accuracy of the major class of land use type is 94.3%, and more than 91.2% for the

Sustainability 2019, 11, x FOR PEER REVIEW 5 of 21 secondary subclasses of land use types [48,49]. The flowchart of the data acquisition process is shown in Figure 2. The original dataset contains 6 major classes and 25 secondary subclasses. This dataset provides convenient and effective data support for the study of land use changes and is widely used by scholars in China [48,49]. In this study, the land use types are reclassified into arable land, forest land, grassland, water, construction land, and unused land, so as to study and analyze the distribution characteristics, spatiotemporal evolution, and driving forces of land use change and evolution. The Sustainability 2020, 12, 3687 5 of 21 socio-economic data were downloaded from the National Bureau of Statistics (http://www.stats.gov.cn/).

Figure 2. TheThe flowchart ﬂowchart of generation of the land use raster data with 1 km resolution.

2.2.2.The Barycenter original Migration dataset contains Model 6 major classes and 25 secondary subclasses. This dataset provides convenient and eﬀective data support for the study of land use changes and is widely used by scholars in ChinaThe [barycenter48,49]. In this migration study, the model land useof land types use are ca reclassiﬁedn effectively into describe arable land, the forestspatial land, and grassland,temporal water,evolution construction of land use land, types and [50]. unused By understanding land, so as to studythe distribution and analyze centers the distribution of various land characteristics, use types spatiotemporalin each study period, evolution, we andcan find driving the forcesspatial of chan landge use of changeland use. and The evolution. barycenter The coordinates socio-economic are datagenerally were represented downloaded by from longitude the National and latitude, Bureau and of Statistics are given (http: by [51]://www.stats.gov.cn /).

2.2.2. Barycenter Migration Model 𝑋 𝐶𝑋 /𝐶 (1) The barycenter migration model of land use can effectively describe the spatial and temporal evolution of land use types [50]. By understanding the distribution centers of various land use types in 𝑌 𝐶 𝑌 /𝐶 (2) each study period, we can find the spatial change of land use. The barycenter coordinates are generally represented by longitude and latitude, and are given by [51]: where 𝑋 and 𝑌 are the longtitude and latitude of the center of a certain land use type in the t-th m m year, 𝐶, 𝑋, and 𝑌 are the area, longitude, Xand latitudeX values, respectively, of the i-th patch in the t-th year, and 𝑚is the total number ofX patchest = (ofC tiaX certaini)/ landCti use type. (1) = = The measurement of the interannual distancei 1 of thei 1movement of a land use type’s barycenter is calculated as follows [52]: Xm Xm Yt = (CtiYi)/ Cti (2) 𝐷 𝐶•𝑋i=1 𝑋 𝑌i=1 𝑌 (3) ′ ′ ′ where Xt and Yt are the longtitude and latitude of the center of a certain land use type in the t-th year, Cwhereti, Xi, Dand representsYi are the the area, barycenter longitude, migratio and latituden distance values, of land respectively, use from oftime the ti -thto patch𝑡 , (𝑋 in,𝑌 the ) andt-th year,(𝑋,𝑌 and) arem theis geographical the total number coordinates of patches of ofthe a barycenter certain land at use these type. times, and C is a constant equal to 111.111The and measurement is the coefficient of the interannualof converting distance the geog ofraphical the movement coordinate of a landunit useinto type’s the plane barycenter distance is calculated(km). as follows [52]: 1 h 2 2i 2 Dt t = C (Xt Xt) + (Yt Yt) (3) 0− • 0 − 0 − where D represents the barycenter migration distance of land use from time t to t0,(Xt0 , Yt0 ) and (Xt, Yt) are the geographical coordinates of the barycenter at these times, and C is a constant equal to 111.111 and is the coefficient of converting the geographical coordinate unit into the plane distance (km). Sustainability 2020, 12, 3687 6 of 21

2.2.3. Intensity Analysis Intensity analysis is a quantitative method used to analyze maps of land categories from several points in time for a single site by considering cross-tabulation matrices, where one matrix summarizes the change at each time interval. It is called “Intensity Analysis” because the method accounts for the intensity of land transitions while answering the following three interrelated questions by examining the degree to which changes are non-uniform at three levels: Interval, category, and transition, to answer the following three questions, respectively: (1) Over what time intervals is the annual rate of overall change relatively slow versus fast? (2) Given the answer to question 1, which land categories are relatively dormant versus active at a given time? (3) Given the answers to questions 1 and 2, which transitions are intensively avoided versus targeted by a given land category over a given time interval? The interval intensity analysis computes the total change size and rate over each time interval, and compares the change rate with a uniform rate of change [35–39]. This is calculated by Equations (4) and (5): PT 1 PJ hPJ i PJ PJ − C C / C t=1 j=1 i=1 tij − tjj j=1 i=1 tij U = 100% (4) YT Y × − 1 where U is the value of a uniform line for time intensity analysis, Ctij represents the areas that transition from category i at time Yt to category j at time Yt+1 (the same below), J is the number of land use categories (the same below), T is the number of time points, t is the index for a time point, which ranges from 1 to T 1, Yt is the year at time point t (the same below), i is the index for a land use − category at an initial time, and j is the index for a land use category at a ﬁnal time. This is continued by considering the following: PJ hPJ i PJ PJ C C / C j=1 i=1 tij − tjj j=1 i=1 tij St = 100% (5) Y + Yt × t 1 − where St is the annual intensity of change for time interval [Yt, Yt+1]. The category level of intensity analysis examines how the intensity of change varies among the land use categories. It computes the intensity of annual gross gains and losses for each category, and then compares them with a uniform intensity. Calculated by Equations (6) and (7), this is expressed as: h i PJ ( ) i= Ctij Ctij / Yt+1 Yt G = 1 − − 100% (6) tj PJ × i=1 Ctij where Gtj is the annual intensity of gross gain of category j for time interval [Yt, Yt+1], and PJ C C /(Y + Yt) j=1 tij − tii t 1 − L = 100% (7) ti PJ × j=1 Ctij where Lti is the annual intensity of gross loss of category i for time interval [Yt, Yt+1]. The transition level of intensity analysis analyzes the intensity of any given transition from one category to another and examines it for a particular time interval when conversion is strong. This is calculated by Equations (8)–(10): h i PJ ( ) i= Ctin Ctnn / Yt+1 Yt W = 1 − − 100% (8) tn PJ hPJ i C C × j=1 i=1 tij − tnj Sustainability 2019, 11, x FOR PEER REVIEW 7 of 21

𝐶⁄𝑌 𝑌 𝑅 100% (9) ∑ 𝐶 where 𝑅 is the annual intensity of transition from category i to category n during time interval [𝑌,𝑌], and

∑ 𝐶𝐶𝑌 𝑌 𝑉 100% (10) ∑ ∑ 𝐶𝐶 Sustainability 2020, 12, 3687 7 of 21

wherewhere W𝑉tn is is the the value value of of uniform uniform intensity intensity of of transition transition to from category categoryn from m non-n to all categoriesnon-m categories at time Yatt duringtime 𝑌 the duringtime interval time interval [Yt, Yt+ 1[],𝑌 and,𝑌n],is and the m land is the use land category use categories that other categoriesthat transitioned have transitioned into other intocategories. (i , n). Finally, In addition, Ctin/(Yt+ Yt) R = 1 − 100% (9) tin 𝐶P⁄J𝑌 𝑌 C × 𝑄 j=1 tij 100% (11) ∑ 𝐶 where Rtin is the annual intensity of transition from category i to category n during time interval [whereY , Y 𝑄], and is the annual intensity of transition from category m to category j during time interval t t+1 [𝑌,𝑌] (𝑗𝑚). PJ C Ctmm /(Y + Yt) j=1 tmj − t 1 − Vtm = 100% (10) PJ PJ 3. Results C C × i=1 j=1 tij − tim where3.1. LandVtm Useis theArea value Change of uniformin the Four intensity Municipalities of transition from category m to all non-m categories at Y Y Y m time Fromt+1 during the percentage time interval of the [ areat, t+ of1], the and differenis thet land use categoriestypes for the that period transitioned 1990–2015 into in other the categories.study areas, Finally, we can see that forest land holds the largest proportion in Beijing, while arable land was Ctmj/(Yt+1 Yt) the largest in the other three municipalitiesQ = (Figure −3). For 100%Tianjin and Shanghai, since they have (11) tmj PJ × famous ports, the land use types with the next lai=rgest1 Ctij proportions were water and construction land. In Beijing, the area of arable land decreased by about 10%, while construction land increased where Qtmj is the annual intensity of transition from category m to category j during time interval from 9.74% to 17.59%, leading to linear trends of –1.512 %/y and 1.505 %/y, from 1990 to 2015, [Yt, Yt+1](j , m). respectively (Figure 3a). Except for the construction land, which has an increasing tendency (a = 3.1.362), Results the extents of the other types of land are decreasing in Tianjin city, with the arable land showing the greatest decrease (Figure 3b). For Shanghai, the arable land showed a significant 3.1.decrease Land Usewith Area a rate Change of –2.883 in the %/yr, Four the Municipalities area decreasing from 62% to 47.67%, while the construction land Fromsignificantly the percentage increased of the(a = area 3.247) of the(Figure diﬀerent 3c). Because land use of types Chongqing’s for the period vast 1990–2015territory and in theits studytopography, areas, wethe can areas see of that arable forest land, land forest holds land, the largest and grassland proportion account in Beijing, for more while than arable 95% land of was the thetotal largest area of in the the municipality. other three municipalitiesThe rate of land (Figure use change3). For in Chongqing Tianjin and was Shanghai, lower than since for they the haveother famousthree municipalities, ports, the land although use types there with was the still next a largestcharacteristic proportions decrease were in water arable and land construction and an increase land. in construction land (Figure 3d).

Figure 3. Cont.

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Figure 3. Area percentage change of land use types in (a) Beijing, (b) Tianjin, (c) Shanghai and (d) Chongqing.Figure 3. Area Dashed percentage lines indicate change the of slopes land ofuse changes types in and (a) are Beijing, denoted (b) as Tianjin, “a”. AL (c)= Shanghaiarable land, and FL (d)= forestChongqing. land, GL Dashed= Grassland, lines indicate WA = water, the slopes CL = ofconstruction changes and land, are denoted UL = unused as “a”. land. AL = arable land, FL = forest land, GL= Grassland, WA = water, CL = construction land, UL = unused land. In Beijing, the area of arable land decreased by about 10%, while construction land increased from 9.74%3.2. Spatial to 17.59%, Distributions leading of to Land linear Us trendse Change of –1.512in the Four %/y Municipalities and 1.505 %/y, from 1990 to 2015, respectively (Figure3a). Except for the construction land, which has an increasing tendency (a = 1.362), the extents It can be seen from Figure 4 that construction land was mainly distributed in the southeast of ofBeijing, the other and typesthe arable of land land are was decreasing also mainly in Tianjin distributed city, within the the southeast arable landand surrounded showing the the greatest urban decreasearea in a (Figuresemicircular3b). Forring, Shanghai, with sporadic the arabledistributi landon showedin the northwest. a significant Moreover, decrease it was with obvious a rate that of –2.883construction %/yr, the land area expanded decreasing outwards from 62% and to 47.67%, occupied while arable the constructionland during land1990–2015. significantly The forest increased land (awas= 3.247) distributed (Figure in3 c).the Because west and of north Chongqing’s of Beijing, vast and territory because and of the its topography,“Sandification the Control areas of Program arable land,for Beijing forest land,and Tianjin and grassland Vicinity” account and the for “Croplands more than to 95% Forests of the or total Grassland area of theProgram”, municipality. the area The of rateforest of landland useincreased change after in Chongqing 2000. The was forests lower and than gr forasslands the other are threedistributed municipalities, in the north although of Tianjin, there waswith still water a characteristic and construction decrease land in mainly arable in land the and middle an increase and southern in construction parts. The land areas (Figure of arable3d). land, 3.2.water, Spatial and Distributions construction of land Land were Use Changelarger in in Shangh the Fourai, Municipalities with water mainly distributed in the north of Shanghai and the construction land in the middle. With increasing urbanization, it was clear that the constructionIt can be land seen expanded from Figure and4 that arable construction land decreased land wasover mainly the study distributed period in in Shanghai. the southeast Because of Beijing,of the mountainous and the arable areas, land wasthe eastern also mainly part distributedof Chongqing in the was southeast mainly andforests surrounded and grasslands, the urban the areacentral in a and semicircular western parts ring, were with sporadicarable land, distribution and the construction in the northwest. land was Moreover, mainly itconcentrated was obvious in that the constructionwest. land expanded outwards and occupied arable land during 1990–2015. The forest land was distributed in the west and north of Beijing, and because of the “Sandification Control Program for Beijing and Tianjin Vicinity” and the “Croplands to Forests or Grassland Program”, the area of forest land increased after 2000. The forests and grasslands are distributed in the north of Tianjin, with water and construction land mainly in the middle and southern parts. The areas of arable land, water, and construction land were larger in Shanghai, with water mainly distributed in the north of Shanghai and the construction land in the middle. With increasing urbanization, it was clear that the construction land expanded and arable land decreased over the study period in Shanghai. Because of the mountainous areas, the eastern part of Chongqing was mainly forests and grasslands, the central and western parts were arable land, and the construction land was mainly concentrated in the west.

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FigureFigure 4. 4.Land Land useuse maps of (a) (a) Beijing, Beijing, (b) (b )Tianjin, Tianjin, (c) (c )Shanghai Shanghai and and (d) (d Chongqing) Chongqing from from 1990 1990 to to 2015. 2015. Figure5 shows the spatial migration trajectories of arable and construction land in the four municipalities.Figure 5 shows The moving the spatial track migration of the barycenter trajectories of of Beijing’s arable and arable construction land over land each in time the segmentfour wasmunicipalities. generally “east-north-south-north-east”.The moving track of the barycenter In 1990,of Beijing’s the barycenter arable land coordinatesover each time of arablesegment land werewas 116.506generally◦E, “east-no 40.042rth-south-north-east”.◦N, which then moved In 1990, to 116.524 the baryce◦E, 40.052nter coordinates◦N by 2015. of arable During land the were period between116.506°E, 1990 40.042°N, and 2015, which the then barycenter moved to of 116.524° arable landE, 40.052°N in Beijing by moved2015. During towards the period the northeast between over a1990 distance and 2015, of 2.29 the km.barycenter The migration of arable land of the in barycenterBeijing moved of thetowards construction the northeast land over was a founddistance to be “west-south-east-southwest”,of 11.46 km. The migration of the and barycenter over the studyof the period,construction it generally land was moved found toto thebe “west-south- southeast for a distanceeast-southwest”, of 0.65 km. and Theover barycenterthe study period, of arable it genera andlly construction moved to the land southeast in Tianjin for a moved distance towards of 13.85 the northeastkm. The andbarycenter southwest of arable with and distances construction of 1.13 land km andin Tianjin 4.39 km, moved respectively. towards the There northeast is a U-shaped and patternsouthwest ofthe with arable distances barycenter of 15.35 migrationkm and 15.11 in km, Shanghai; respectively. speciﬁcally, There is from a U-shaped 1990 to pattern 2000, itof movedthe southwardsarable barycenter and then migration to the in east Shanghai; from 2000 specific to 2005,ally, andfrom then 1990 towardsto 2000, it the moved north southwards from 2005 toand 2015. then to the east from 2000 to 2005, and then towards the north from 2005 to 2015. In general, the In general, the barycenter of arable land in Shanghai moved to the northeast and its migration distance barycenter of arable land in Shanghai moved to the northeast and its migration distance was 3.65 km. was 1.01 km. Construction land moved 2.76 km towards the southeast from 1990 to 2015. From Construction land moved slightly towards the southeast from 1990 to 2015. From Figure 4, it can be Figure4, it can be seen that in Chongqing, there were two stages for the migration of the barycenter of seen that in Chongqing, there were two stages for the migration of the barycenter of arable land, that arable land, that is, it ﬁrst moved to the southwest between 1990 to 2005, and then to the northeast is, it first moved to the southwest between 1990 to 2005, and then to the northeast from 2005 to 2015; fromhence, 2005 between to 2015; 1990 hence, to 2015, between it moved 1990 to to the 2015, south it movedfor a distance to the of south 75.79 for km, a distancewhile the of barycenter 0.26 km, whileof thethe barycenter construction of land the construction moved toward land the moved northeast toward by 64.28 the km. northeast by 12.72 km.

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Figure 5. Migrations of arable and construction land barycenters in the four municipalities from 1990 Figure 5. Migrations of arable and construction land barycenters in the four municipalities from to 2015. The black arrow lines indicate the moving distances. 1990 to 2015. The black arrow lines indicate the moving distances. 3.3. Intensity Analysis Results of Land Use Change in Four Municipalities

The results of the interval intensity analysis are shown in Figure6. It can be seen that the uniform intensities of LUCC in Beijing, Tianjin, Shanghai, and Chongqing were 0.49%, 0.50%, 0.76%, and 0.15%,

Sustainability 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/sustainability Sustainability 2019, 11, x FOR PEER REVIEW 11 of 21 ofSustainability land use2020 change, 12, 3687 in Shanghai was most rapid from 2000 to 2005, then 1990–1995 and 2005–2010,11 of 21 with the slowest in 1995–2000 (Figure 6c). Compared with Beijing, Tianjin, and Shanghai, the LUCC inrespectively, Chongqing duringwas slower the study during period. the study Land period. use change However, was mostfor this rapid region, in Shanghai, the bars on followed the right by exceedBeijing the and uniform Tianjin, intensity with Chongqing line from being 2000 to the 2015, slowest. which indicates that the rates of land use changes were relatively fast during this time (Figure 6d).

Figure 6. Cont.

Sustainability 2020, 12, 3687 12 of 21 Sustainability 2019, 11, x FOR PEER REVIEW 12 of 21

Figure 6. Time interval intensities of land use changes in (a) Beijing, (b) Tianjin, (c) Shanghai and (d) Figure 6. Time interval intensities of land use changes in (a) Beijing, (b) Tianjin, (c) Shanghai and (d) Chongqing during 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015. The bars that extend Chongqing during 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015. The bars that extend to the left of zero show the gross area of overall changes in each interval, while the bars that extend to to the left of zero show the gross area of overall changes in each interval, while the bars that extend the right of zero show intensity of annual area of change within each time interval. The dashed line to the right of zero show intensity of annual area of change within each time interval. The dashed line was the uniform intensity. If the bar exceeds the uniform intensity line, the change in the land use in was the uniform intensity. If the bar exceeds the uniform intensity line, the change in the land use in that time interval was relatively fast; otherwise, it was relatively slow. that time interval was relatively fast; otherwise, it was relatively slow. The area and intensity of land use change in Beijing were the largest and fastest in 1990–1995, followedFor the by 2000–2005,category intensity while the analysis changes of were Beijing the smallest(Figure 7a), and we slowest found in that 1995–2000 the uniform (Figure intensity6a). The wasland the use changeslargest during in Tianjin 1990–1995 were relatively and approximately fast in 2000–2005 equal and to 1990–1995 1.37%; the (Figure gains6 b).of Thewater rate and of constructionland use change land in were Shanghai active, was while most the rapid losses from of arable 2000 toand 2005, grassland then 1990–1995 were active, and and 2005–2010, the gain withand lossthe slowestof forest in were 1995–2000 both dormant. (Figure 6 c). Compared with Beijing, Tianjin, and Shanghai, the LUCC in ChongqingThe category was slower intensity during of theland study use change period. in However, Tianjin forwas this largest region, in 2000–2005, the bars on theat about right exceed1.14%, wherethe uniform the losses intensity of forest, line grassland, from 2000 and to 2015, water which were indicatesactive, while that their the rates gains of were land dormant. use changes The weregain intensityrelatively of fast construction during this land time was (Figure greater6d). than its loss and was active, while for the arable land, the intensityFor theof loss category was larger intensity than analysis the gain, of Beijingbut was (Figure not active7a), we at foundall (Figure that the7b). uniform In the interval intensity 2000– was 2005,the largest the uniform during intensity 1990–1995 of andland approximately use category change equal toin 1.37%;Shanghai the was gains 1.29%, of water in which and construction the gains of forest,land were grassland, active, and while construction the losses of land arable were and active, grassland as were were the active, losses andof arable the gain land and and loss grassland. of forest Inwere 1990–1995, both dormant. the losses of arable land and grassland and the gain in construction land were active, whileThe the categorychanges intensityof forest and of land water use were change dormant. in Tianjin In addition was largest to the in 2000–2005,active loss of at aboutgrassland 1.14%, in 2005–2010where the and losses the of active forest, gain grassland, of grassland and waterin 2010–2015, were active, the losses while of their arable gains and were forest dormant. land and Thethe gainsgain intensity in the extent of construction of construction land wasland greater were acti thanve its over loss two and intervals. was active, In while1995–2000, for the the arable losses land, of arablethe intensity and forest of loss land was and larger the gain than of construction the gain, but land was were not active active atin allShanghai (Figure (Figure7b). In 7c). the The interval gain intensity2000–2005, of theconstruction uniform intensityland in Chongqing of land use was category active changein each time in Shanghai interval was(Figure 1.29%, 7d). in which the gains of forest, grassland, and construction land were active, as were the losses of arable land and grassland. In 1990–1995, the losses of arable land and grassland and the gain in construction land were active, while the changes of forest and water were dormant. In addition to the active loss of grassland in 2005–2010 and the active gain of grassland in 2010–2015, the losses of arable and forest land and the gains in the extent of construction land were active over two intervals. In 1995–2000, the losses of arable and forest land and the gain of construction land were active in Shanghai (Figure7c). The gain intensity of construction land in Chongqing was active in each time interval (Figure7d).

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Figure 7. Category intensity analysis for the (a) (a) Beijing, (b) (b) Tianjin, ((c)c) Shanghai and ((d)d) ChongqingChongqing for 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015. The bars that are above the zero axis shows the intensity of gains, and those below the axis showshow losses.losses. The da dashedshed line is the uniform intensity. IfIf thethe bar bar exceeds exceeds the the line, line, it indicates it indicates that th theat changethe change of a certain of a certain land use land in thatuse timein that interval time wasinterval active; was otherwise, active; otherwis the changee, the was change dormant. was dormant. From the interval intensity analysis and category intensity analysis, we found the rate of overall From the interval intensity analysis and category intensity analysis, we found the rate of overall change of land use and the intensity of gain or loss of each land use type for the four study areas in each change of land use and the intensity of gain or loss of each land use type for the four study areas in time interval. Then, we used the transition intensity analysis to determine the dominant conversion of each time interval. Then, we used the transition intensity analysis to determine the dominant land use change. These results are shown in Table1. The arable land and grassland in Beijing were conversion of land use change. These results are shown in Table 1. The arable land and grassland in the types mainly reduced in each period and converted into construction land. While the increase of Beijing were the types mainly reduced in each period and converted into construction land. While construction land mainly comes from arable land, the intensity of conversion from arable land into the increase of construction land mainly comes from arable land, the intensity of conversion from construction land was much greater than that of the conversion of grassland to construction land. In arable land into construction land was much greater than that of the conversion of grassland to 1990–1995 and 2000–2005, the forest land was added to from arable land and grassland, and in the other construction land. In 1990–1995 and 2000–2005, the forest land was added to from arable land and three periods, it was mainly converted into grassland and construction land. For the land use changes in grassland, and in the other three periods, it was mainly converted into grassland and construction Tianjin, the arable, forest, and grassland types were mainly converted into construction land. Between land. For the land use changes in Tianjin, the arable, forest, and grassland types were mainly 1990–1995 and 2000–2005, the forest land of Shanghai increased, mainly coming from arable land and converted into construction land. Between 1990–1995 and 2000–2005, the forest land of Shanghai grassland, with the arable land decreasing in extent and being converted into construction land. In increased, mainly coming from arable land and grassland, with the arable land decreasing in extent Chongqing, the arable land was dominated by its conversion into construction land during each period. and being converted into construction land. In Chongqing, the arable land was dominated by its Forest land was added to by the conversion of grassland in 1990–1995, 2000–2005, and 2005–2010, and conversion into construction land during each period. Forest land was added to by the conversion of was reduced by its conversion into grassland or construction land in 1995–2000 and 2010–2015. The grassland in 1990–1995, 2000–2005, and 2005–2010, and was reduced by its conversion into grassland or construction land in 1995–2000 and 2010–2015. The grassland mainly increased by the conversion of arable land in 2000–2005, and converted into forest land during the other periods.

Sustainability 2020, 12, 3687 14 of 21 grassland mainly increased by the conversion of arable land in 2000–2005, and converted into forest land during the other periods.

Table 1. Conversion of dominant land uses during the ﬁve time intervals. “-”indicates the land use type converted into other types, while ”+” indicates that the land use type was added to from other types, and ”—”indicates that the conversion was not active. AL = arable land, FL = forest land, GL = grassland, CL = construction land.

Beijing 1990–1995 1995–2000 2000–2005 2005–2010 AL -,CL -,CL -,CL -,CL FL +,AL,GL -,GL +,AL,GL -,CL GL -, CL -,CL -,CL -, CL CL +,AL +,AL +,AL +,AL Tianjin 1990–1995 1995–2000 2000–2005 2005–2010 AL -,CL -,GL,CL -,CL -, CL FL -,CL— -, CL -, CL GL -,CL -,UL -, CL -, CL CL +,AL,GL +,AL,FL +,AL,GL +,AL,GL Shanghai 1990–1995 1995–2000 2000–2005 2005–2010 AL -,CL -,CL -,CL -, CL FL +,AL -,CL +,AL,GL -, CL GL — — — — CL +,AL,GL +,AL,FL +,AL,FL +,AL, Chongqing 1990–1995 1995–2000 2000–2005 2005–2010 AL -, CL -, CL -CL -, CL FL +,GL -,GL, CL +,GL +,GL GL -,FL -,FL,CL +,AL -,FL,CL CL +,AL +,AL +,AL +,AL

4. Discussion

4.1. The Impact of Socio-Economic Development on Land Use Change Land use change is the result of both natural and human activities [14]. However, over time scales of several decades or even hundreds of years, the impact of natural factors is mainly embodied in cumulative and background effects, while the impact of human factors as the main cause of land use change is relatively active and easy to detect [53,54]. Among the many factors of human activities, socio-economic development and population growth are particularly important for the structure of land use change. This leads to changes differing between regions according to the degree of socio-economic development and population growth. Figure8 shows the GDP and population change of the four municipalities, all of which have grown significantly in the period between 1990 and 2015. Among them, Shanghai has the highest rate of GDP and population growth, 978.06 108 yuan/y and 50.90 104 persons/y, respectively. The change rates × × of GDP and population for each of the study intervals (1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015) were 219.76%, 90.89%, 93.83%, 85.63%, and 46.36% and 6%, 13.76%, 17.51%, 21.82%, and 4.89%, respectively. We observe from Figure6 that the land use change intensity in Shanghai was the highest in 2000–2005, which corresponds to when the rates of economic and population growth were the largest. During different study periods in Beijing and Tianjin, the intensity of land use change was also active during times of high economic and population growth rates, where the GDP and population change rates in Beijing were 880.52 108 yuan/y and 47.18 104 persons/y, respectively, and × × 628.63 108 yuan/y and 24.77 104 persons/y, respectively, in Tianjin. Chongqing’s GDP was the lowest × × and its population the largest, while its GDP and population growth were also the slowest of the four study areas, leading to the lowest land use change intensity (0.15%). LUCC here was active in Sustainability 2020, 12, 3687 15 of 21

2000–2005, during which the rate of GDP and population growth in Chongqing were the largest of the examined study periods at 128.55% and 4.24%, respectively, but they were lower than the change rates of the other regions during the same period. Sustainability 2019, 11, x FOR PEER REVIEW 2 of 2

Figure 8. Changes in the (a) GDP, (b) population and (c) three industries during 1990–2015 for the Figure 8. Changes in the (a) GDP, (b) population and (c) three industries during 1990–2015 for the four municipalities. four municipalities. In addition, we can see from Figure8c that the proportion of primary industry was reduced and that secondary and tertiary industries increased, with the proportion made up of tertiary industry in Beijing and Shanghai being larger than for secondary industries. These changes in industrial structure will inevitably lead to a sharp increase in the population, the expansion of the port (for the case of Shanghai), the establishment of a large number of industrial plants, and an increase in infrastructure. This will lead to the decrease in available arable and forest land, and an increase in industrial and residential land. In general, the rates of GDP and population growth in Shanghai were the fastest of the four municipalities, while the intensity of land use changes was the largest (0.76%). By contrast, the rates of GDP and population growth in Chongqing were the slowest, while the intensity of LUCC was the smallest, indicating that where there was more economic and population growth, the intensity of land use change was greater in Chongqing.

4.2. The Impact of Government Policy on Land Use Change A series of policies implanted by the state have had an important impact on land use change. From the intensity analysis, we found that the rate of land use change was relatively high in 1990–1995, but in 1995–2000, the intensity changes suddenly decreased, which was also found in the research of Liu et al. [55]. The cause of this phenomenon is that the 1990s were an important period in China’s overall transition from planned to market economy. In the ﬁrst ﬁve years of the 1990s, the all-around development of the social economy laid a certain material foundation for the development of urbanization, and promoted the development of China’s real estate industry. This formed a strong

Sustainability 2019, 11, x FOR PEER REVIEW 16 of 21

A series of policies implanted by the state have had an important impact on land use change. From the intensity analysis, we found that the rate of land use change was relatively high in 1990– 1995, but in 1995–2000, the intensity change suddenly decreased, which was also found in the research of Liu et al. [55]. The cause of this phenomenon is that the 1990s were an important period inSustainability China’s overall2020, 12, 3687transition from planned to market economy. In the first five years of the 1990s,16 ofthe 21 all-around development of the social economy laid a certain material foundation for the development ofmomentum urbanization, behind and promoted “development the development zone fever” of and Chin “reala’s real estate estate fever”, industry. leading This to formed a large a lossstrong of momentumcultivated land behind and “development the rapid expansion zone fever” of rural and residential “real estate areas. fever”, Since leading then, landto a managementlarge loss of cultivatedpolicies and land regulations and the have rapid been expansion promulgated of rural to e ﬀresidentialectively contain areas. the Since rapid then, change land of landmanagement use, such policiesas the “Regulations and regulations on the have Protection been promulgated of Basic Farmland”, to effectively “Dynamic contain Equilibrium the rapid change of Total of Cultivated land use, suchLand”, as andthe “Balance“Regulations of Cultivated on the Protection Land Occupation of Basic andFarmland”, Compensation” “Dynamic in Equilibrium 1994, 1996, andof Total 1998, Cultivatedrespectively Land”, (Figure and9). Since“Balance 2000, of with Cultivated China’s accessionLand Occupation to the World and Trade Compensation” Organization, in the1994, country 1996, andhas entered1998, respectively a stage of rapid(Figure industrialization 9). Since 2000, with and China’s urbanization, accession which to the has World driven Trade the rapid Organization, expansion theof urban country land. has In entered 2010, China a stage issued of rapid the planning industri ofalization principal and functional urbanization, zoning which regulations, has driven dividing the rapidthe land expansion space into of keyurban development land. In 2010, zones, China protected issued developmentthe planning of zones, principal restricted functional development zoning regulations,zones, and prohibited dividing the development land space zones into [56key]. Underdevelopment the policy zones, control protected of the maindevelopment functional zones, areas, restrictedthe urban development construction landzones, intensity and prohibited of Beijing, devel Shanghai,opment and zones Tianjin [56]. in Under 2010–2015 the policy was lower control than of thethat main during functional the previous areas, 10 the years urban [57 ].construction land intensity of Beijing, Shanghai, and Tianjin in 2010–2015 was lower than that during the previous 10 years [57].

FigureFigure 9. 9. TheThe impact impact of of national national policies policies and and urban urban plans plans on on land land use change.

SinceSince the the start start of of the 21st century, China has implemented a series of regional development strategies,strategies, such such as as the the “western development”, development”, “the “the revitalization revitalization of of old old industrial industrial bases bases in in the the northeast”,northeast”, and and the the “rise “rise of of the the central region”, which have promoted the rapid development of of the socialsocial economy economy and have had had an an important important impact impact on the dynamics dynamics of LUCC [48]. [48]. Beijing Beijing and and Tianjin Tianjin havehave carried carried out out a a series series of of projects, projects, such such as as the the “Three “Three North” North” shelter shelter forest forest development, development, ecological ecological agricultureagriculture development, the conversion of farmland farmland to to forests, forests, the the “Beijing “Beijing Tianjin Tianjin sandstorm sandstorm source source control“,control“, andand greeninggreening around around the the capital capital and and Taihang Taihang Mountain. Mountain. These These have have produced produced obvious obvious eﬀects effectson regional on regional land use land change, use change, especially especially in Beijing in during Beijing 2010–2013, during 2010–2013, with the areawith of the garden area of and garden forest andland forest showing land a growthshowing trend. a growth Since thetrend. reform Since and th thee reform opening and up the of theopening economy, up of in the order economy, to promote in ordereconomic to promote development, economic Shanghai development, began to Shanghai expropriate began and to rent expropriate land in the and suburbs rent land in the in the 1980s. suburbs Over the past 20 years, a total of 9.6 104 km2 of land has been expropriated, mainly for land acquisition, in the 1980s. Over the past 20 years,× a total of 9.6×104 km2 of land has been expropriated, mainly for landand mostacquisition, of which and was most used offor which non-productive was used for purposes, non-productive such as purposes, municipal such and realas municipal estate projects and realand theestate construction projects and of publicthe cons facilities.truction Thus, of public the urban facilities. land Th useus, pattern the urban has signiﬁcantly land use pattern changed. has significantlySince 1997, changed. the year Chongqing became a municipality, the government has promulgated laws and regulationsSince 1997, the on farmlandyear Chongqing protection became and landa munici use,pality, which the reduced government the rate has of cultivatedpromulgated land laws to a andcertain regulations extent. Furthermore,on farmland protection the western and development, land use, which the Threereduced Gorges the rate project, of cultivated and the land policy to ofa certainreturning extent. farmlands Furthermore, to forests the have western resulted developme in changingnt, the land Three use patterns Gorges inproject, Chongqing. and the In policy addition, of the overall planning of the government and the functional orientation of each district and county have also had a profound impact on urban land use change [44–47]. Sustainability 2019, 11, x FOR PEER REVIEW 17 of 21

returning farmlands to forests have resulted in changing land use patterns in Chongqing. In addition, Sustainability 2020 12 the overall planning, , 3687 of the government and the functional orientation of each district and county17 of 21 have also had a profound impact on urban land use change [44–47]. 4.3. The Impact of Major Urban Events on Land Use Change 4.3. The Impact of Major Urban Events on Land Use Change The impact of major urban events on land use type distribution was mainly caused by the change in theThe urban impact spatial of major and economic urban events scales. on land Moreover, use type significant distribution events was play mainly a more caused important by the change role in thein the spatial urban expansion spatial and of economic long time scales. series. Moreover, The 2008 Beijingsignificant Olympic events Games play a andmore the important 2010 Shanghai role in Worldthe spatial Expo expansion are the main of long reasons time for series. the change The 2008 of land Beijing use patternsOlympic around Games the and meeting the 2010 sites Shanghai [58,59], andWorld caused Expo land are the use main changes reasons in the for two the regions change to of be land relatively use patterns high in around the period the meeting 2000–2005. sites Figure [58,59], 10 showsand caused the di landfferences use changes arising in before the two starting regions construction to be relatively and after highthe in the completion period 2000–2005. of major events,Figure which,10 shows in the Beijing, differences covered arising the surroundings before starting of cons thetruction national and aquatics after the center completion and national of major stadium, events, and,which, in in Shanghai, Beijing, covered the Expo the Park,surroundings exhibition of the hall, nati Expoonal Axis, aquatics etc. center It can and easily national be seen stadium, from and, this figurein Shanghai, that, as the a resultExpo Park, of these exhibition events, hall, significant Expo Axis, changes etc. hadIt can taken easily place be seen in the from urban this landscapesfigure that, ofas thesea result municipalities. of these events, In addition significant to these changes large-scale had taken activities, place within the the urban rapid landscapes development of ofthese the economy,municipalities. the launch In addition of various to these construction large-scale projects activities, also with affected the rapid the land development use changes of the around economy, these constructionthe launch of projects. various construction projects also affected the land use changes around these construction projects.

FigureFigure 10. 10.Image Image showing showing the the di differencesﬀerences in in Beijing Beijing (above (above)) and and Shanghai Shanghai (below (below)) before before and and after after the Olympicthe Olympic Games Games and World and World Expo, Expo, respectively. respectively. The triangles The triangles in the olderin the photos older photos indicate indicate the features the thatfeatures were therethat were prior there to the prior construction to the construction of the labeled of the elements labeled inelem theents more in recentthe more photos. recent photos.

5. Conclusions 5. Conclusions In this study, the spatiotemporal variations of land use change in Beijing, Tianjin, Shanghai, and ChongqingIn this werestudy, analyzed the spatiotemporal based on five variations time segments of land of use land change use classification in Beijing, Tianjin, data from Shanghai, 1990 to 2015. and ThisChongqing was done were by intensityanalyzed analysis based on and five a barycenter time segments migration of land model. use classification This allowed adata qualitative from 1990 study to of2015. the drivingThis was forces done behind by intensity such changes. analysis The and results a barycenter indicated thatmigration the land model. use change This intensityallowed ina Shanghaiqualitative was study the mostof the rapid, driving followed forces bybehind Beijing such and changes. Tianjin, withThe results Chongqing indicated being that the slowest.the land Theuse decreasechange intensity in arable in land Shanghai and the was increase the most in construction rapid, followed land were by Beijing both active and Tianjin, in each timewith periodChongqing in all fourbeing municipalities; the slowest. The the decrease arable land in arable was mainly land and transformed the increase into in construction construction land. land Correspondingly,were both active thein each moving time distances period of in the all barycenter four municipalities; of construction the land arable were land longer was than mainly those oftransformed arable land. into The intensity of land use change was affected by economic development, population growth, policy, etc.; Sustainability 2020, 12, 3687 18 of 21 where there was a high rate of economic and population growth, the intensity of land use change was large. In this paper, the dynamic changes of land use in the four municipalities were analyzed based on intensity analysis for three levels: Interval level, category level, and transition level. This calculates the contribution of each category to the total change, the loss and gain of different categories, and their intensities for each period. It also determines the extent of the conversion and intensity between each category, providing more detailed information for the study of land use change. A great deal of previous research found that this method was superior to the widely used dynamic degree model and the Markov model [40,60]. However, the intensity analysis also has shortcomings and must be improved. For example, this method does not consider data uncertainty, the influences of the overlap between different categories, or their spatial distributions. Therefore, spatial variation analysis remains inadequate. To solve these problems, the intensity analysis framework must be improved by adding information about the spatial distribution of the various categories of land use into the analytical process so that the characteristics of both the spatial and temporal variations can be analyzed. In addition, land use change is affected by many factors, as it occurs under the background of global climate change and the acceleration of urbanization. Hence, the integration of high-resolution remote sensing data and natural and socio-economic data should be strengthened in future research, so as to analyze the driving forces and comprehensively identify the mechanisms of land use change, while, in turn, applying these results to forecasting and the regulation of future trends of land use.

Author Contributions: All authors contributed significantly to this manuscript. Y.B. (Yuhai Bao) was responsible for the original idea and the theoretical aspects of the paper. G.B. and X.H. were responsible for the data collection and preprocessing, A.R. and Y.B. (Yongbin Bao) were responsible for the methodology design, and S.T. drafted the manuscript; all authors read and revised the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the Special Fund for Science and Technology for Research Investigation (No. 2017FY101301-4), the Key Program of National Natural Science Foundation of China (No. 61631011), and the Research Foundation for Advanced Talents of Inner Mongolia Normal University (2018YJRC007, 2019YJRC002). Acknowledgments: We would like to thank the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDS, http://www.resdc.cn) for providing the land use data set. Conflicts of Interest: The authors declare no conflict of interest.

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